1. Field of the Invention
This invention relates a computer system and method for predicting a future value of a series of product data.
2. Description of the Prior Art
Previously the approach of statistical process control (SPC) has been used to analyse manufacturing and other processes. Data about products produced in a manufacturing process are analysed in order to make inferences about the manufacturing process itself. For example, if the manufacturing process was for making confectionery, samples of confectionery would be drawn off at certain time intervals and analysed. Measurements for various parameters would be taken, for example, the weight of the confectionery items, the sugar content or other factors. Data from the samples would then be used to make inferences about the whole population of manufactured products and the manufacturing process. Typically, statistics such as the mean and standard deviation or range were calculated for the sample data for each parameter, and these statistics compared for different samples. For example, if the mean was observed to move outside a certain threshold range an xe2x80x9cout of controlxe2x80x9d flag would be triggered to alert the factory staff to a problem in the manufacturing process. If trends were observed in the data, for example, an increase in the mean, the user could be alerted to this fact and then an investigation carried out.
Several problems with these statistical approaches to process control are known. For example, an inference is made that the data sets fit a standard type of distribution, such as a normal or Poisson distribution. However, this is rarely the case for process control data in which many outlying values are typically observed and which are often bimodal or show other irregular distributions. Also, data is obtained from a small sample of the manufactured products and used to make inferences about the whole population of manufactured products. This means that the statistics calculated using SPC type methods often are not an accurate reflection of the manufacturing process being analysed. Where products exhibit a high degree of consistency of performance then statistical examination of data is adequate, however, some products such as electric circuits have been found to exhibit performance results that do not fit statistical distributions, even though the data from these products fall within predetermined performance margins.
Process control is a difficult problem for manufacturers; it involves analysing the state of the manufacturing process and knowing how to adjust the manufacturing process in the light of the analysis in order to achieve efficiency and desired outputs. The manufacturer is faced with the problem of producing products that are within certain xe2x80x9ctolerancexe2x80x9d limits with respect to various parameters (for example, weight of confectionery bars) whilst at the same time reducing waste. For example, in order to manufacture confectionery bars that are all of a given minimum stated weight, the majority of the bars have to be produced with a weight that is greater than that minimum. If the manufacturer were able to produce confectionery bars all with a particular weight a great cost saving could be made. However, because of the limitations of current methods of process control this cannot be achieved. Often factors to do with the manufacturing process itself are too difficult to be measured practically and so measurements from the product themselves are taken. These measurements are sometimes analysed statistically and by making simple comparisons but information about the process is not provided quickly enough and with enough precision to enable the manufacturing process to be adjusted. The information provided about the process is about the xe2x80x9crecent pastxe2x80x9d behaviour of that process and this means that there is always a xe2x80x9ctime lagxe2x80x9d between receiving data about the process and taking any corrective action.
Another problem is that test data that is routinely collected in production tests on the factory floor are often not suitable for statistical analysis. This is because the data sets are often small, incomplete, discontinuous and because they contain outlying values. However, this type of data is typically all that is available for process control. Many manufacturers measure their products against a predetermined test regime and hence a wealth of data is routinely generated. Often because no suitable method for analysing this kind of data is available, the data is simply stored away xe2x80x9cfor the recordxe2x80x9d and this is a waste of resources. Methods that can be used to analyse this type of data are typically time consuming and do not allow the data to be reviewed in close to real time.
Another problem in process control is being able to deal with the fact that the inputs to the process vary. For example, if components are supplied to a manufacturer for assembly into a final product, those components may vary from batch to batch and from supplier to supplier. However, it is very difficult to analyse how the components vary and this is time consuming and expensive. Also, it is difficult to determine what effect variations in the components may have on the manufacturing process that is being controlled. These problems increase for more complex products that involve many components, such as circuit boards. For this reason, many manufacturers aim to limit variability by attempting to strictly control all the initial build conditions which includes the supply base. This is often not possible if it is necessary to vary the supplier for other reasons, for example to attain a good price or to achieve continuity of supply. Many manufacturers of electronic systems rely heavily upon their suppliers to ensure that materials and components used in the fabrication of products are compliant to specification. Often, electronic components are not examined before they enter factories. Investment programmes for test equipment at the component level have shown that it is not practical to distinguish between batches of components and also that the instances of non-compliant components are negligible. For these reasons many manufacturing companies have wound down their incoming component inspection processes. Instances do occur where manufactured products exhibit changes in performance that are attributed to changes in the components but no effective way of dealing with this problem has been found.
A particular problem in process control involves the situation where a manufacturing process is set up in a particular location, such as the USA, and it is required to set up the same process in a new location, say Canada, in order to produce the same quality of product with the same efficiency. It is typically very difficult to set up the new process in such a way that the same quality of product is produced with the same efficiency because of the number of factors that influence the process.
Failure mode effect analysis is another problem in process control. In this case, a failure occurs in the manufacturing or other process and it is required to analyse why this has occurred and what corrective action should be taken. Current methods for dealing with failure mode effect analysis include schematic examination and fault injection techniques but these are not satisfactory because of the problems with the data mentioned above.
JP8314530 describes a failure prediction apparatus which uses chaos theory based methods. A physical quantity, such as an electrical signal, showing the condition of a single installation is measured repeatedly at regular intervals in order to collect a time series of data. This time series of data is then used to reconfigure an attractor which is used to predict future values of the time series. These predicted values are compared with observed values in order to predict failure of the installation. This system is disadvantageous in many respects. The input data must be repeated measurements from a single apparatus taken at regular intervals. However, in practice it is often not possible to obtain measurements at regular intervals. Also, JP8314530 does not address the problems of dealing with product data and non time series data such as product data obtained from many products which will vary. Also, JP8314530 is concerned with failure prediction only and not with other matters such as monitoring performance and detecting changes in behaviour of a process. Moreover, JP8314530 does not describe the process of identifying nearest neighbour vectors and determining corresponding vectors for these.
It is accordingly an object of the present invention to provide a computer system and method for predicting a future value of a series of product data which overcomes or at least mitigates one or more of the problems noted above.
According to a first aspect of the present invention there is provided a method of predicting a future value of a series of product data comprising the steps of:
(i) forming a set of vectors wherein each vector comprises a number of successive values of the series of product data;
(ii) identifying from said set of vectors, a current vector which comprises a most recent value of the series of product data;
(iii) identifying at least one nearest neighbour vector from said set of vectors, wherein for each nearest neighbour vector a measure of similarity between that nearest neighbour vector and the current vector is less than a threshold value;
(iv) for each nearest neighbour vector, determining a corresponding vector, each corresponding vector comprising values of the series of product data that are a specified number of data values ahead of the data values of the nearest neighbour vector in said series of product data; and
(v) calculating the predicted future value on the basis of at least some of the corresponding vector(s); wherein said series of product data comprises a plurality of values each measured from a different product.
A corresponding computer system for predicting a future value of a series of product data comprises:
(i) a processor arranged to form a set of vectors wherein each vector comprises a number of successive values of the series of product data;
(ii) an identifier arranged to identify from said set of vectors, a current vector which comprises a most recent value of the series of product data;
(iii) a second identifier arranged to identify at least one nearest neighbour vector from said set of vectors, wherein for each nearest neighbour vector a measure of similarity between that nearest neighbour vector and the current vector is less than a threshold value;
(iv) a determiner arranged to determine, for each nearest neighbour vector, a corresponding vector, each corresponding vector comprising values of the series of product data that are a specified number of data values ahead of the data values of the nearest neighbour vector in said series of product data; and
(v) a calculator arranged to calculate the predicted future value on the basis of at least some of the corresponding vector(s); wherein said series of product data comprises a plurality of values each measured from a different product.
This provides the advantage that product data from a manufacturing process can be analysed and used to provide a prediction about performance of the process in the future. This removes any xe2x80x9ctime lagxe2x80x9d between obtaining data about the manufacturing process and allows immediate modification of the manufacturing process to reduce waste. This reduces manufacturing costs and improves efficiency. The manufacturing process can be effectively controlled using the product data despite the fact that this data may not fit a recognised statistical distribution and is not suitable for statistical analysis. The effects of inputs to the manufacturing process, such as new suppliers and new batches of raw materials is monitored or controlled without the need to carry out measurements or tests on the inputs. In the case that the manufacturing process fails the failure situation can be analysed by comparing the predicted and actual product data.
According to another aspect of the present invention there is provided a method of substantially determining an attractor structure from a series of product data comprising the steps of:
(i) forming a set of vectors wherein each vector comprises a number of successive values of the series of product data;
(ii) calculating a set of eigenvectors and a set of eigenvalues from said set of vectors using the method of principal components analysis; and
(iii) transforming the said set of vectors on the basis of said set of eigenvectors; wherein said series of product data comprises a plurality of values each measured from a different product.
A corresponding computer system for substantially determining an attractor structure from a series of product data comprises:
(i) a processor arranged to form a set of vectors wherein each vector comprises a number of successive values of the series of product data;
(ii) a calculator arranged to calculate a set of eigenvectors and a set of eigenvalues from said set of vectors using the method of principal components analysis; and
(iii) a transformer arranged to transform the said set of vectors on the basis of said set of eigenvectors; wherein said series of product data comprises a plurality of values each measured from a different product.
This provides the advantage that product data can be analysed by determining an attractor structure. If no effective attractor structure is identified for a given parameter then this parameter is known not to be a good input for the prediction process. This enables the costs of obtaining product data to be reduced because ineffective product data parameters can be eliminated. Another advantage is that two separate manufacturing processes that are intended to produce the same product can be compared by comparing their attractor structures. Adjustments can then be made to the processes until the attractor structures are substantially identical and this helps to ensure that the same quality of product is produced in both manufacturing sites with the same efficiency.
Preferably said series of product data comprise values that were measured at irregular time intervals. This provides the advantage that the prediction and analysis processes are effective for product data that is measured at irregular time intervals.